A Path Computing Scheme for Low-Latency Requirement of Medical Big Data Task

被引：1
|
作者：
Zhang X. [1 ]
Ren Z. [1 ]
Hu J. [1 ]
Zhang Y. [2 ]
Zhang H. [1 ]
机构：
[1] State Key Laboratory of Integrated Services Networks, Xidian University, Xi'an
[2] National Key Laboratory of Science and Technology on Space Microwave (Xi'an), CAST, Xi'an
来源：
Hsi-An Chiao Tung Ta Hsueh/Journal of Xi'an Jiaotong University | 2020年 / 54卷 / 02期
关键词：
Cloud and fog network; Mapping; Medical big data; Path computing;
D O I：
10.7652/xjtuxb202002015
中图分类号：
学科分类号：
摘要：
A path computing scheme for low-latency task is proposed to solve the problem that there exist high communication load and high task processing latency when cloud computing is applied to medical big data processing. The scheme firstly constructs the medical big data task into a directed acyclic graph that is composed of multiple subtasks with explicit input and output relationships. Then, a cloud and fog network architecture is designed, in which network edge devices such as switches and routers in hospitals form a fog computing layer. The computing capacity of fog nodes is used to gradually complete the big data task in the process of end-to-end directional data transmission. A task mapping strategy based on the discrete binary particle swarm optimization (BPSO) algorithm is proposed to deploy the medical big data task to a hospital network. The big data task in the form of a directed acyclic graph is mapped into a topology graph of the hospital fog network to find an appropriate computing path for the task and to minimize the latency. Simulation results and a comparison with the cloud computing show that the proposed path computing scheme reduces the latency by more than 50% when the amount of data is 5-10 Mb. © 2020, Editorial Office of Journal of Xi'an Jiaotong University. All right reserved.
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页码：119 / 126
页数：7
相关论文
共 21 条
  • [1] Meng L., Ma J., Liu J., Et al., Research and application of big data in medical field, Chinese Medical Equipment Journal, 39, 10, (2018)
  • [2] Zhang Y., Chang Z., Ji J., Using big data to promote research on "Internet+medical" development, Times Finance, 718, 36, (2018)
  • [3] Chen T., Chen Y., Chen J., Et al., Flu trend prediction based on massive data analysis, 2018 IEEE 3rd International Conference on Cloud Computing and Big Data Analysis, pp. 304-308, (2018)
  • [4] Archenaa J., Anita E.A.M., A survey of big data analytics in healthcare and government, Procedia Computer Science, 50, pp. 408-413, (2015)
  • [5] Shu Y., Chen Y., Ji Z., Et al., Research progress of big data of healthcare and medical treatment, China Medical Equipment, 16, 1, pp. 149-153, (2019)
  • [6] Zhang S., Shi S., Yang X., Discussion on the challenges and strategies of healthcare big data application, Chinese Journal of Health Informatics and Management, 15, 6, (2018)
  • [7] Guo J., Based on cloud computing and big data of hospital informatization construction, Electronic Technology & Software Engineering, 150, 4, (2019)
  • [8] Vukobratovic D., Jakovetic D., Skachek V., Et al., CONDENSE: a reconfigurable knowledge acquisition architecture for future 5G IoT, IEEE Access, 4, pp. 3360-3378, (2016)
  • [9] Gallager R., Finding parity in a simple broadcast network, IEEE Transactions on Information Theory, 34, 2, pp. 176-180, (1988)
  • [10] Kushilevitz E., Mansour Y., Computation in noisy radio networks, Proceedings of the 19989th Annual ACM SIAM Symposium on Discrete Algorithms, pp. 236-243, (1998)
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